There were an estimated 2.5 million PET scans in the U.S. alone in 2012. There were an estimated 900,000 PET scans in Western Europe in 2011. Worldwide, there continues to be rapid growth of PET scans. Therefore, improvements in PET-reconstruction quality have a very high impact on patient care.
Super-resolution is a well-established technique, often used in image processing. This technique requires super-sampled data as input. It has been demonstrated that super-sampling, such as “wobbling” the scanner by a few millimeters, can substantially improve image quality and reduce artifacts; however, there are practical problems with implementing wobbling for whole-body scanners. For example, wobbling the scanner requires additional hardware for moving the scanner or patient. Moreover, current PET scanners no longer do this for a combination of reasons: (1) it was believed that super-sampling no longer added any useful information after technological breakthroughs allowed the pixel size of the detectors to be reduced; (2) manufacturers dropped wobbling because it is mechanically cumbersome; and (3) it added cost to have this additional hardware for wobbling the scanner.
FIG. 1 and FIG. 2 illustrate current techniques of super-resolution reconstructions and super-sampling. Digital images such as PET reconstructions inherently average over an area or volume to produce pixel/volume values. FIG. 1 illustrates several examples of the effect of that averaging by shifting the edge of the first pixel between down samples ((b)-(e)). Because the pixels in the images represent averages of overlapping but different regions, there is greater information content in the set of images than in any individual image. Super-sampling allows one to recover higher-quality images, depending on the resolution of the imaging system.
FIG. 1 (a) is a high resolution image (512×512; 0.125 mm pixels) of a hot-rod phantom (50 mm diameter; rod diameters: 1.2, 1.6, 2.4, 3.2, 4.0, and 4.8 mm) on a warm background (hot:back=5:1). The image is blurred (1.5 mm FWHM Gaussian) and down-sampled to 64×64 (1 mm) pixels in (b)-(e), but with relative shifts in the horizontal and vertical direction (in mm) of (0,0), (−2.5,0), (0,−2.5), and (−2.5,−2.5), respectively. This half-integer bin shifting (2.5 instead of 2) results in averaging different high-res pixels in (a) into lower resolution pixels for (b)-(e).
FIG. 2 illustrates the result of super-resolution using a post-reconstruction algorithm to combine the four samples in FIG. 1 ((b)-(e)), each on a 64×64 grid, back into a 512×512 grid (as in the high-resolution image in FIG. 1(a)). The original resolution is not fully restored (due to Gaussian blur, which is not yet modeled in the algorithm, and information loss from down sampling), but the image's improved resolution signifies information recovery.
A method is desired that allows for the acquisition of super-sampled data at very low cost, without use of a post-reconstruction algorithm as in the example of FIGS. 1 and 2 or additional hardware in the scanner. The present invention addresses these needs in the art.